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finance

Goals: Update goal

update_goal
    Update an existing financial goal.

    Args:
        goal_id: The ID of the goal to update
        name: New goal name (optional)
        target_amount: New target amount (optional, may auto-complete if <= current amount)
        target_date: New target date in YYYY-MM-DD format (optional)
        status: New status - active, completed, paused, cancelled (optional)
        color: New hex color code (optional)
        notes: New notes (optional)
        priority: New priority level (optional)

    Returns:
        Updated goal details
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
colorNo
notesNo
statusNo
goal_idYes
priorityNo
target_dateNo
target_amountNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide no read-only, idempotency, or destructiveness hints, so the description carries the burden. The description discloses useful behavior: target_amount 'may auto-complete if <= current amount' and status has specific allowed values, plus a Returns line. However, it leaves important mutation semantics unstated, such as whether updates are partial, whether null clears fields, or whether status transitions are validated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a compact Google-style docstring with a one-line purpose, a dense parameter list, and a Returns line. Every section earns its place, especially because the schema itself contains no parameter descriptions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter mutation tool with no annotations and no output schema, the description covers all parameters, the return value, and a notable side effect. It is still missing explicit partial-update semantics and error/precondition behavior, which would make it fully complete for an agent invoking it correctly without extra inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description compensates by documenting all 8 parameters. It adds format details for target_date (YYYY-MM-DD), enumerates status choices, specifies hex color format, and highlights target_amount auto-completion. It is slightly weaker on priority type/range and null semantics, but still provides substantial meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise verb and resource: 'Update an existing financial goal.' It is clearly distinguishable from sibling tools like create_goal, delete_goal, and list_goals, which operate on the same resource with different actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'existing financial goal' implies this tool is for modifying goals that already exist, but the description never explicitly says when to prefer it over create_goal, delete_goal, or contribute_to_goal. There are no exclusions or alternative-name mentions, only an implied usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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